Agency and Organisation: The Dialectics of Nature and Life

In recent decades, there have been major theoretical changes within evolutionary biology. In this dissertation, I critically reconstruct these developments through philosophy to assess how it may inform these debates. The overall aim is to show the mutual relevance between current trends in biology and the dialectical approach to nature. I argue that the repetition of the neglected tradition of organicism is anticipated both by a dialectical tradition within science and by Hegel’s philosophy – and that these theories may together inform the ongoing shift within evolutionary biology called the Extended Evolutionary Synthesis (EES). I stage the discussion by outlining the tenets and history of the modern synthesis (MS) and the alternative: the extended evolutionary synthesis (EES). It takes us into topics such as autonomy, organisation, reduction, and autopoiesis. Based on these discussions, I make the case that the most promising alternative to the MS is the so-called organisational approach formulated within theoretical biology and apply dialectics to strengthen this claim. In my view, they share a fundamental premise: Biology must surpass the physical worldview and adopt a more complex model to comprehend life as an ongoing regeneration of organisation and an expression of self-determination. To bring out the philosophical stakes of this shift, I take on Hegel’s writings on nature, life, and purposiveness and relate them to contemporary thinkers. The main contribution of this work lies not in a particularly novel reading of any of the theories I examine but in bringing them together – both within philosophy and biology and between them – and systematically mapping how philosophy and the humanities should deal with the natural sciences. The new kind of naturalism suggested here, which places life at its core, also calls for another scientific ideal which strives for unification without subsumption or eradication of differences

2022-2023 Xavier University Undergraduate and Graduate University Catalog

https://www.exhibit.xavier.edu/coursecatalog/1275/thumbnail.jp

2020-2021 Xavier University Undergraduate and Graduate University Catalog

https://www.exhibit.xavier.edu/coursecatalog/1273/thumbnail.jp

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Queensland University of Technology: Handbook 2019

The Queensland University of Technology handbook gives an outline of the faculties and subject offerings available that were offered by QUT

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems